shup2 <-- "Hello to you!"Clicker Questions
to go along with
Modern Data Science with R, 3rd edition by Baumer, Kaplan, and Horton
Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, and Tibshirani
- The reason to take random samples is:1
- to make cause and effect conclusions
- to get as many variables as possible
- it’s easier to collect a large dataset
- so that the data are a good representation of the population
- I have no idea why one would take a random sample
- The reason to allocate/assign explanatory variables is:2
- to make cause and effect conclusions
- to get as many variables as possible
- it’s easier to collect a large dataset
- so that the data are a good representation of the population
- I have no idea what you mean by “allocate/assign” (or “explanatory variable” for that matter)
- Approximately how big is a tweet?3
- 0.01Kb
- 0.1Kb
- 1Kb
- 100Kb
- 1000Kb = 1Mb
- \(R^2\) measures:4
- the proportion of variability in vote margin as explained by tweet share.
- the proportion of variability in tweet share as explained by vote margin.
- how appropriate the linear part of the linear model is.
- whether or not particular variables should be included in the model.
- R / R Studio / Quarto5
- all good
- started, progress is slow and steady
- started, very stuck
- haven’t started yet
- what do you mean by “R”?
- Git / GitHub6
- all good
- started, progress is slow and steady
- started, very stuck
- haven’t started yet
- what do you mean by “Git”?
- Which of the following includes talking to the remote version of GitHub?7
- changing your name (updating the YAML)
- committing the file(s)
- pushing the file(s)
- some of the above
- all of the above
- What is the error?8
- poor assignment operator
- unmatched quotes
- improper syntax for function argument
- invalid object name
- no mistake
- What is the error?9
- poor assignment operator
- unmatched quotes
- improper syntax for function argument
- invalid object name
- no mistake
3shup <- "Hello to you!"- What is the error?10
- poor assignment operator
- unmatched quotes
- improper syntax for function argument
- invalid object name
- no mistake
shup4 <- "Hello to you!- What is the error?11
- poor assignment operator
- unmatched quotes
- improper syntax for function argument
- invalid object name
- no mistake
shup5 <- date()- What is the error?12
- poor assignment operator
- unmatched quotes
- improper syntax for function argument
- invalid object name
- no mistake
shup6 <- sqrt 10- Do you keep a calendar / schedule / planner?13
- Yes
- No
- Do you keep a calendar / schedule / planner? If you answered “Yes” …14
- Yes, on Google Calendar
- Yes, on Calendar for macOS
- Yes, on Outlook for Windows
- Yes, in some other app
- Yes, by hand
- Where should I put things I’ve created for the HW (e.g., data, .ics file, etc.)15
- Upload into remote GitHub directory
- In the local folder which also has the R project
- In my Downloads
- Somewhere on my Desktop
- In my Home directory
- The goal of making a figure is…16
- To draw attention to your work.
- To facilitate comparisons.
- To provide as much information as possible.
- A good reason to make a particular choice of a graph is:17
- Because the journal / field has particular expectations for how the data are presented.
- Because some variables naturally fit better on some graphs (e.g., numbers on scatter plots).
- Because that graphic displays the message you want as optimally as possible.
- Why are the points orange?18
- R translates “navy” into orange.
- color must be specified in
geom_point() - color must be specified outside the
aes()function - the default plot color is orange
ggplot(data = Births78,
aes(x = date, y = births, color = "navy")) +
geom_point() +
ggtitle("US Births in 1978")- Why are the dots blue and the lines colored?19
- dot color is given as “navy”, line color is given as
wday. - both colors are specified in the
ggplot()function. - dot coloring takes precedence over line coloring.
- line coloring takes precedence over dot coloring.
- dot color is given as “navy”, line color is given as
- Setting vs. Mapping. If I want information to be passed to all data points (not variable):20
- map the information inside the
aes()function. - set the information outside the
aes()function
- map the information inside the
- The Snow figure was most successful at:21
- making the data stand out
- facilitating comparison
- putting the work in context
- simplifying the story
- The Challenger figure(s) was(were) least successful at:22
- making the data stand out
- facilitating comparison
- putting the work in context
- simplifying the story
- The biggest difference between Snow and the Challenger was:23
- The amount of information portrayed.
- One was better at displaying cause.
- One showed the relevant comparison better.
- One was more artistic.
- Caffeine and Calories. What was the biggest concern over the average value axes?24
- It isn’t at the origin.
- They should have used all the data possible to find averages.
- There wasn’t a random sample.
- There wasn’t a label explaining why the axes were where they were.
:::
Footnotes
- so that the data are a good representation of the population
- to make cause and effect conclusions
- about 0.1Kb. Turns out that 3.5 billion tweets * 0.1Kb = 350Gb (0.35 Tb). My laptop is pretty good, and it has 36 Gb of memory (RAM) and 4 Tb of storage. It would not be able to work with 3.5 billion tweets.
- the proportion of variability in vote margin as explained by tweet share.
wherever you are, make sure you are communicating with me when you have questions!↩︎
wherever you are, make sure you are communicating with me when you have questions!↩︎
- pushing the file(s)
- poor assignment operator
- invalid object name
- unmatched quotes
- no mistake
- improper syntax for a function argument
- I mean, the right answer has to be Yes, right!??!
no right answer here!↩︎
- In the local folder which also has the R project. It could be on the Desktop or the Home directory, but it must be in the same place as the R project. Do not upload files to the remote GitHub directory or you will find yourself with two different copies of the files.
Yes! All the responses are reasons to make a figure.↩︎
- Because that graphic displays the message you want as optimally as possible.
- color must be specified outside the
aes()function
- color must be specified outside the
- dot color is specified as “navy”, line color is specified as
wday.
- dot color is specified as “navy”, line color is specified as
- set the information outside the
aes()function
- set the information outside the
answers may vary. I’d say c. putting the work in context. Others might say b. facilitating comparison or d. simplifying the story. However, I don’t think a correct answer is a. making the data stand out.↩︎
- making the data stand out
- One showed the relevant comparison better.
- It isn’t at the origin. in combination with d. There wasn’t a label explaining why the axes were where they were. The story associated with the average value axes is not clear to the reader.